3. A Classification Problem Using DNNs

Activity 3.01: Building an ANN

Solution:

1. Import the following libraries:

   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.utils import shuffle
   from sklearn.metrics import accuracy_score
   import torch
   from torch import nn, optim
   import torch.nn.functional as F
   import matplotlib.pyplot as plt
   torch.manual_seed(0)

2. Read the previously prepared dataset, which should have been named dccc_prepared.csv:

   data = pd.read_csv("dccc_prepared.csv")
   data.head()

   The output should be as follows:

   Figure 3.14: dccc_prepared.csv

3. Separate the features from the target:

   X = data.iloc[:,:-1]
   y = data["default payment next month"]

4. Using scikit-learn's train_test_split function, split the dataset into training, validation, and testing sets. Use a 60:20:20 split ratio. Set random_state to 0:

   X_new, X_test, \
   y_new, y_test = train_test_split(X, y, test_size=0.2, \
     ...